Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [2]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:04, 2.26MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:06<00:00, 8.98KFile/s]
Downloading celeba: 1.44GB [05:58, 4.02MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[3]:
<matplotlib.image.AxesImage at 0x7f64e4166f28>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x7f64e4036e10>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [37]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [38]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(tf.float32, 
                                 (None, image_width, image_height, image_channels), 
                                 name='input_real') 
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, (None), name='input_z')

    return inputs_real, inputs_z, learning_rate

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/home/demo/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/home/demo/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py", line 3, in <module>\n    app.launch_new_instance()', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 474, in start\n    ioloop.IOLoop.instance().start()', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py", line 887, in start\n    handler_func(fd_obj, events)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 275, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 275, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 390, in execute_request\n    user_expressions, allow_stdin)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 501, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-38-5de3a2d9a72d>", line 23, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/demo/code/DeepLearning/project5/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/demo/code/DeepLearning/project5/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/demo/code/DeepLearning/project5/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/demo/code/DeepLearning/project5/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/demo/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [47]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.1
    # "As we are using Batch Normalization, there is no need to use dropouts. 
    # However if we wish to use dropouts we should do proper testing keeping 
    # different values of keep_prob between 0.6 and 0.9."
    #keep_prob = 0.8
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 32, 5, strides=1, padding='SAME', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32))
        relu1 = tf.maximum(alpha * x1, x1)
        #28x28x32
        
        x2 = tf.layers.conv2d(relu1, 64, 5, strides=2, padding='SAME', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32))
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        #dp2 = tf.nn.dropout(relu2, keep_prob)
        #14x14x64
        
        x3 = tf.layers.conv2d(relu2, 128, 5, strides=1, padding='SAME', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32))
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        #dp3 = tf.nn.dropout(relu3, keep_prob)
        #14x14x128
        
        x4 = tf.layers.conv2d(relu3, 256, 5, strides=2, padding='SAME', kernel_initializer=tf.contrib.layers.xavier_initializer(uniform=True, seed=None, dtype=tf.float32))
        bn4 = tf.layers.batch_normalization(x4, training=True)
        relu4 = tf.maximum(alpha * bn4, bn4)
        #dp4 = tf.nn.dropout(relu4, keep_prob)
        #7x7x256
                        
        flat = tf.reshape(relu4, (-1, 7*7*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        #1

    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [48]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    #reuse_param=True 
    alpha = 0.1
    # "As we are using Batch Normalization, there is no need to use dropouts. 
    # However if we wish to use dropouts we should do proper testing keeping 
    # different values of keep_prob between 0.6 and 0.9."
    #keep_prob = 0.8
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*256)
        
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 7x7x256 now
        
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        #dp2 = tf.nn.dropout(x2, keep_prob)
        # 14x14x128 now
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=1, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        #dp3 = tf.nn.dropout(x3, keep_prob)
        # 14x14x64 now
        
        x4 = tf.layers.conv2d_transpose(x3, 32, 5, strides=2, padding='same')
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = tf.maximum(alpha * x4, x4)
        #dp4 = tf.nn.dropout(x4, keep_prob)
        # 28x28x32 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x4, out_channel_dim, 3, strides=1, padding='same')
        # 28x28x3 now
        
        out = tf.tanh(logits)
        
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [41]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [42]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
   
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [43]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [44]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # Build Model
    image_width = data_shape[1]
    image_height = data_shape[2]
    image_channels = data_shape[3]
    input_real, input_z, lr = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, image_channels)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    steps = 0

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # scaling the batch images by a factor of 2 to make it in the range of [-1 1]
                batch_images = batch_images * 2
                steps+=1
        
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
            
                _ = sess.run(d_train_opt, feed_dict={input_real:batch_images, input_z: batch_z, lr:learning_rate})
                # run the optimization for the generator twice to make sure that the discriminator loss does not go to zero.
                _ = sess.run(g_train_opt, feed_dict={input_z:batch_z, lr:learning_rate, input_real:batch_images})
                _ = sess.run(g_train_opt, feed_dict={input_z:batch_z, lr:learning_rate, input_real:batch_images})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                
                    _ = show_generator_output(sess, 1, input_z, data_shape[3], data_image_mode)


                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [49]:
batch_size = 64
z_dim = 100
learning_rate = 0.0005 #learning rate between 0.0002 and 0.0008
beta1 = 0.3 #Beta1 between 0.2 and 0.5 


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 6.4885... Generator Loss: 0.0039
Epoch 1/2... Discriminator Loss: 3.7304... Generator Loss: 0.0692
Epoch 1/2... Discriminator Loss: 3.1829... Generator Loss: 0.0816
Epoch 1/2... Discriminator Loss: 2.6308... Generator Loss: 0.1347
Epoch 1/2... Discriminator Loss: 2.4669... Generator Loss: 0.1568
Epoch 1/2... Discriminator Loss: 2.6000... Generator Loss: 0.1377
Epoch 1/2... Discriminator Loss: 2.3526... Generator Loss: 0.1726
Epoch 1/2... Discriminator Loss: 2.3484... Generator Loss: 0.1700
Epoch 1/2... Discriminator Loss: 2.2423... Generator Loss: 0.1948
Epoch 1/2... Discriminator Loss: 2.6262... Generator Loss: 0.1230
Epoch 1/2... Discriminator Loss: 2.2360... Generator Loss: 0.1934
Epoch 1/2... Discriminator Loss: 2.1403... Generator Loss: 0.2241
Epoch 1/2... Discriminator Loss: 2.2430... Generator Loss: 0.1791
Epoch 1/2... Discriminator Loss: 2.2083... Generator Loss: 0.1977
Epoch 1/2... Discriminator Loss: 2.1051... Generator Loss: 0.2457
Epoch 1/2... Discriminator Loss: 1.8451... Generator Loss: 0.2880
Epoch 1/2... Discriminator Loss: 2.1816... Generator Loss: 0.2198
Epoch 1/2... Discriminator Loss: 2.0459... Generator Loss: 0.2375
Epoch 1/2... Discriminator Loss: 1.9557... Generator Loss: 0.2590
Epoch 1/2... Discriminator Loss: 2.1309... Generator Loss: 0.1990
Epoch 1/2... Discriminator Loss: 1.7239... Generator Loss: 0.3547
Epoch 1/2... Discriminator Loss: 2.0197... Generator Loss: 1.5448
Epoch 1/2... Discriminator Loss: 1.5608... Generator Loss: 0.7551
Epoch 1/2... Discriminator Loss: 1.3974... Generator Loss: 0.8003
Epoch 1/2... Discriminator Loss: 1.5161... Generator Loss: 0.8767
Epoch 1/2... Discriminator Loss: 1.5459... Generator Loss: 0.6617
Epoch 1/2... Discriminator Loss: 2.1580... Generator Loss: 1.7081
Epoch 1/2... Discriminator Loss: 1.5872... Generator Loss: 0.7343
Epoch 1/2... Discriminator Loss: 1.4295... Generator Loss: 0.6284
Epoch 1/2... Discriminator Loss: 1.9357... Generator Loss: 0.2587
Epoch 1/2... Discriminator Loss: 3.1740... Generator Loss: 2.1494
Epoch 1/2... Discriminator Loss: 1.8560... Generator Loss: 0.3027
Epoch 1/2... Discriminator Loss: 1.7478... Generator Loss: 1.5917
Epoch 1/2... Discriminator Loss: 1.5644... Generator Loss: 0.7989
Epoch 1/2... Discriminator Loss: 1.4972... Generator Loss: 0.8940
Epoch 1/2... Discriminator Loss: 1.7723... Generator Loss: 0.9819
Epoch 1/2... Discriminator Loss: 1.8247... Generator Loss: 0.2884
Epoch 1/2... Discriminator Loss: 1.6591... Generator Loss: 0.3936
Epoch 1/2... Discriminator Loss: 1.5326... Generator Loss: 0.4927
Epoch 1/2... Discriminator Loss: 3.2657... Generator Loss: 0.0693
Epoch 1/2... Discriminator Loss: 1.4711... Generator Loss: 0.4748
Epoch 1/2... Discriminator Loss: 2.3868... Generator Loss: 0.1618
Epoch 1/2... Discriminator Loss: 1.6451... Generator Loss: 0.3977
Epoch 1/2... Discriminator Loss: 1.5124... Generator Loss: 0.4727
Epoch 1/2... Discriminator Loss: 2.1864... Generator Loss: 0.1982
Epoch 1/2... Discriminator Loss: 2.4780... Generator Loss: 0.1655
Epoch 1/2... Discriminator Loss: 1.7911... Generator Loss: 0.3156
Epoch 1/2... Discriminator Loss: 1.7865... Generator Loss: 0.3252
Epoch 1/2... Discriminator Loss: 1.3330... Generator Loss: 1.5941
Epoch 1/2... Discriminator Loss: 1.5727... Generator Loss: 1.1338
Epoch 1/2... Discriminator Loss: 1.3165... Generator Loss: 0.9826
Epoch 1/2... Discriminator Loss: 1.4708... Generator Loss: 0.5579
Epoch 1/2... Discriminator Loss: 1.6245... Generator Loss: 0.4011
Epoch 1/2... Discriminator Loss: 1.8324... Generator Loss: 1.7782
Epoch 1/2... Discriminator Loss: 1.7409... Generator Loss: 0.3217
Epoch 1/2... Discriminator Loss: 2.0783... Generator Loss: 0.2389
Epoch 1/2... Discriminator Loss: 1.1443... Generator Loss: 0.8787
Epoch 1/2... Discriminator Loss: 1.7579... Generator Loss: 1.9386
Epoch 1/2... Discriminator Loss: 1.3402... Generator Loss: 0.7486
Epoch 1/2... Discriminator Loss: 1.3396... Generator Loss: 1.1716
Epoch 1/2... Discriminator Loss: 1.4261... Generator Loss: 0.9193
Epoch 1/2... Discriminator Loss: 1.9467... Generator Loss: 0.2576
Epoch 1/2... Discriminator Loss: 1.5546... Generator Loss: 0.4403
Epoch 1/2... Discriminator Loss: 1.6123... Generator Loss: 1.0865
Epoch 1/2... Discriminator Loss: 1.4084... Generator Loss: 1.1578
Epoch 1/2... Discriminator Loss: 1.5234... Generator Loss: 1.3375
Epoch 1/2... Discriminator Loss: 1.3555... Generator Loss: 0.6666
Epoch 1/2... Discriminator Loss: 1.3532... Generator Loss: 0.7803
Epoch 1/2... Discriminator Loss: 2.3572... Generator Loss: 1.7972
Epoch 1/2... Discriminator Loss: 1.3226... Generator Loss: 0.9551
Epoch 1/2... Discriminator Loss: 1.7589... Generator Loss: 0.3399
Epoch 1/2... Discriminator Loss: 1.6123... Generator Loss: 0.4128
Epoch 1/2... Discriminator Loss: 1.8728... Generator Loss: 0.2900
Epoch 1/2... Discriminator Loss: 1.9351... Generator Loss: 0.2748
Epoch 1/2... Discriminator Loss: 1.3539... Generator Loss: 0.5820
Epoch 1/2... Discriminator Loss: 1.9041... Generator Loss: 2.3076
Epoch 1/2... Discriminator Loss: 1.0582... Generator Loss: 1.0779
Epoch 1/2... Discriminator Loss: 1.8436... Generator Loss: 0.3165
Epoch 1/2... Discriminator Loss: 1.3774... Generator Loss: 0.8141
Epoch 1/2... Discriminator Loss: 1.6456... Generator Loss: 0.3708
Epoch 1/2... Discriminator Loss: 1.5480... Generator Loss: 0.4289
Epoch 1/2... Discriminator Loss: 1.1905... Generator Loss: 0.7978
Epoch 1/2... Discriminator Loss: 1.9488... Generator Loss: 0.2636
Epoch 1/2... Discriminator Loss: 1.7360... Generator Loss: 0.3788
Epoch 1/2... Discriminator Loss: 1.3948... Generator Loss: 1.3098
Epoch 1/2... Discriminator Loss: 1.2540... Generator Loss: 1.2297
Epoch 1/2... Discriminator Loss: 1.3855... Generator Loss: 1.0357
Epoch 1/2... Discriminator Loss: 1.2864... Generator Loss: 1.2228
Epoch 1/2... Discriminator Loss: 1.5414... Generator Loss: 1.5858
Epoch 1/2... Discriminator Loss: 1.3727... Generator Loss: 0.6326
Epoch 1/2... Discriminator Loss: 1.9799... Generator Loss: 0.2954
Epoch 1/2... Discriminator Loss: 1.3435... Generator Loss: 1.3438
Epoch 1/2... Discriminator Loss: 1.4029... Generator Loss: 0.6994
Epoch 2/2... Discriminator Loss: 1.2316... Generator Loss: 0.7435
Epoch 2/2... Discriminator Loss: 1.5484... Generator Loss: 0.4285
Epoch 2/2... Discriminator Loss: 1.3100... Generator Loss: 0.8143
Epoch 2/2... Discriminator Loss: 1.7413... Generator Loss: 0.3322
Epoch 2/2... Discriminator Loss: 1.4106... Generator Loss: 0.5783
Epoch 2/2... Discriminator Loss: 1.4376... Generator Loss: 0.5481
Epoch 2/2... Discriminator Loss: 2.2237... Generator Loss: 0.2100
Epoch 2/2... Discriminator Loss: 1.4038... Generator Loss: 0.5593
Epoch 2/2... Discriminator Loss: 1.6240... Generator Loss: 0.4030
Epoch 2/2... Discriminator Loss: 1.4332... Generator Loss: 1.4231
Epoch 2/2... Discriminator Loss: 1.2844... Generator Loss: 1.3268
Epoch 2/2... Discriminator Loss: 1.3379... Generator Loss: 1.3153
Epoch 2/2... Discriminator Loss: 1.3716... Generator Loss: 1.1522
Epoch 2/2... Discriminator Loss: 1.2734... Generator Loss: 1.7285
Epoch 2/2... Discriminator Loss: 1.2950... Generator Loss: 1.7828
Epoch 2/2... Discriminator Loss: 1.2354... Generator Loss: 1.2750
Epoch 2/2... Discriminator Loss: 1.8090... Generator Loss: 0.3342
Epoch 2/2... Discriminator Loss: 1.3363... Generator Loss: 0.8935
Epoch 2/2... Discriminator Loss: 1.3662... Generator Loss: 0.5375
Epoch 2/2... Discriminator Loss: 1.2217... Generator Loss: 0.7201
Epoch 2/2... Discriminator Loss: 2.2717... Generator Loss: 0.2030
Epoch 2/2... Discriminator Loss: 1.6383... Generator Loss: 0.4135
Epoch 2/2... Discriminator Loss: 1.6130... Generator Loss: 0.3953
Epoch 2/2... Discriminator Loss: 1.7222... Generator Loss: 0.3464
Epoch 2/2... Discriminator Loss: 1.6247... Generator Loss: 0.4384
Epoch 2/2... Discriminator Loss: 1.5870... Generator Loss: 0.4140
Epoch 2/2... Discriminator Loss: 1.8977... Generator Loss: 0.3050
Epoch 2/2... Discriminator Loss: 2.3664... Generator Loss: 0.1888
Epoch 2/2... Discriminator Loss: 1.2707... Generator Loss: 0.8459
Epoch 2/2... Discriminator Loss: 1.1892... Generator Loss: 0.9468
Epoch 2/2... Discriminator Loss: 1.2620... Generator Loss: 0.6562
Epoch 2/2... Discriminator Loss: 1.6324... Generator Loss: 0.4121
Epoch 2/2... Discriminator Loss: 1.3124... Generator Loss: 1.8751
Epoch 2/2... Discriminator Loss: 1.3554... Generator Loss: 0.6041
Epoch 2/2... Discriminator Loss: 1.7012... Generator Loss: 0.3802
Epoch 2/2... Discriminator Loss: 1.5387... Generator Loss: 0.4529
Epoch 2/2... Discriminator Loss: 1.9530... Generator Loss: 0.2813
Epoch 2/2... Discriminator Loss: 1.2528... Generator Loss: 0.7773
Epoch 2/2... Discriminator Loss: 1.0238... Generator Loss: 1.5052
Epoch 2/2... Discriminator Loss: 1.1790... Generator Loss: 1.1470
Epoch 2/2... Discriminator Loss: 1.2727... Generator Loss: 0.6328
Epoch 2/2... Discriminator Loss: 1.6733... Generator Loss: 0.3714
Epoch 2/2... Discriminator Loss: 1.3594... Generator Loss: 0.5963
Epoch 2/2... Discriminator Loss: 1.8661... Generator Loss: 0.3038
Epoch 2/2... Discriminator Loss: 1.1250... Generator Loss: 0.8342
Epoch 2/2... Discriminator Loss: 1.9469... Generator Loss: 0.2857
Epoch 2/2... Discriminator Loss: 1.6311... Generator Loss: 0.3985
Epoch 2/2... Discriminator Loss: 1.2876... Generator Loss: 1.4529
Epoch 2/2... Discriminator Loss: 1.4314... Generator Loss: 0.5377
Epoch 2/2... Discriminator Loss: 1.2678... Generator Loss: 0.8436
Epoch 2/2... Discriminator Loss: 1.4828... Generator Loss: 2.2629
Epoch 2/2... Discriminator Loss: 1.1131... Generator Loss: 1.0243
Epoch 2/2... Discriminator Loss: 0.8814... Generator Loss: 1.3229
Epoch 2/2... Discriminator Loss: 1.9882... Generator Loss: 0.2700
Epoch 2/2... Discriminator Loss: 1.4438... Generator Loss: 0.5089
Epoch 2/2... Discriminator Loss: 1.5805... Generator Loss: 0.4021
Epoch 2/2... Discriminator Loss: 1.1367... Generator Loss: 1.3856
Epoch 2/2... Discriminator Loss: 1.5144... Generator Loss: 0.4552
Epoch 2/2... Discriminator Loss: 2.0447... Generator Loss: 0.2526
Epoch 2/2... Discriminator Loss: 1.2482... Generator Loss: 0.6673
Epoch 2/2... Discriminator Loss: 1.2944... Generator Loss: 0.6349
Epoch 2/2... Discriminator Loss: 1.8381... Generator Loss: 0.3220
Epoch 2/2... Discriminator Loss: 1.7260... Generator Loss: 0.3376
Epoch 2/2... Discriminator Loss: 1.1570... Generator Loss: 0.7503
Epoch 2/2... Discriminator Loss: 1.3126... Generator Loss: 0.5815
Epoch 2/2... Discriminator Loss: 1.0215... Generator Loss: 0.9226
Epoch 2/2... Discriminator Loss: 1.3618... Generator Loss: 0.6237
Epoch 2/2... Discriminator Loss: 1.1225... Generator Loss: 0.8318
Epoch 2/2... Discriminator Loss: 1.4208... Generator Loss: 0.5471
Epoch 2/2... Discriminator Loss: 1.0625... Generator Loss: 1.8210
Epoch 2/2... Discriminator Loss: 2.3200... Generator Loss: 0.2041
Epoch 2/2... Discriminator Loss: 1.0621... Generator Loss: 1.6150
Epoch 2/2... Discriminator Loss: 1.1060... Generator Loss: 0.8421
Epoch 2/2... Discriminator Loss: 1.2290... Generator Loss: 0.9462
Epoch 2/2... Discriminator Loss: 1.2654... Generator Loss: 0.9294
Epoch 2/2... Discriminator Loss: 1.1647... Generator Loss: 1.0366
Epoch 2/2... Discriminator Loss: 1.0788... Generator Loss: 1.7125
Epoch 2/2... Discriminator Loss: 1.1223... Generator Loss: 0.7994
Epoch 2/2... Discriminator Loss: 1.3046... Generator Loss: 1.3590
Epoch 2/2... Discriminator Loss: 1.3840... Generator Loss: 0.5778
Epoch 2/2... Discriminator Loss: 1.1069... Generator Loss: 0.8856
Epoch 2/2... Discriminator Loss: 1.6090... Generator Loss: 0.5219
Epoch 2/2... Discriminator Loss: 1.0129... Generator Loss: 1.2227
Epoch 2/2... Discriminator Loss: 1.3834... Generator Loss: 0.5422
Epoch 2/2... Discriminator Loss: 1.0925... Generator Loss: 0.9149
Epoch 2/2... Discriminator Loss: 1.4764... Generator Loss: 0.4944
Epoch 2/2... Discriminator Loss: 1.8882... Generator Loss: 0.3450
Epoch 2/2... Discriminator Loss: 1.1170... Generator Loss: 0.8891
Epoch 2/2... Discriminator Loss: 1.1830... Generator Loss: 0.9480
Epoch 2/2... Discriminator Loss: 1.2203... Generator Loss: 1.7789
Epoch 2/2... Discriminator Loss: 0.8928... Generator Loss: 1.9341
Epoch 2/2... Discriminator Loss: 1.1527... Generator Loss: 0.7784
Epoch 2/2... Discriminator Loss: 1.6812... Generator Loss: 0.3749
Epoch 2/2... Discriminator Loss: 1.0020... Generator Loss: 1.8202

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [50]:
batch_size = 64
z_dim = 100
learning_rate = 0.0005 #learning rate between 0.0002 and 0.0008
beta1 = 0.3 #Beta1 between 0.2 and 0.5 


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.3260... Generator Loss: 2.9686
Epoch 1/1... Discriminator Loss: 2.0260... Generator Loss: 1.2081
Epoch 1/1... Discriminator Loss: 1.1617... Generator Loss: 1.1518
Epoch 1/1... Discriminator Loss: 2.4913... Generator Loss: 0.1855
Epoch 1/1... Discriminator Loss: 2.1467... Generator Loss: 0.2205
Epoch 1/1... Discriminator Loss: 2.0282... Generator Loss: 0.2754
Epoch 1/1... Discriminator Loss: 2.1082... Generator Loss: 0.2387
Epoch 1/1... Discriminator Loss: 1.3089... Generator Loss: 1.4390
Epoch 1/1... Discriminator Loss: 2.2911... Generator Loss: 0.2130
Epoch 1/1... Discriminator Loss: 1.9089... Generator Loss: 0.4450
Epoch 1/1... Discriminator Loss: 2.4671... Generator Loss: 0.1508
Epoch 1/1... Discriminator Loss: 1.5548... Generator Loss: 0.9096
Epoch 1/1... Discriminator Loss: 1.5452... Generator Loss: 0.8450
Epoch 1/1... Discriminator Loss: 2.8213... Generator Loss: 0.1036
Epoch 1/1... Discriminator Loss: 1.8910... Generator Loss: 0.3077
Epoch 1/1... Discriminator Loss: 1.7043... Generator Loss: 0.5827
Epoch 1/1... Discriminator Loss: 1.8010... Generator Loss: 0.3415
Epoch 1/1... Discriminator Loss: 1.6868... Generator Loss: 0.3737
Epoch 1/1... Discriminator Loss: 1.8786... Generator Loss: 0.3286
Epoch 1/1... Discriminator Loss: 1.3652... Generator Loss: 0.9388
Epoch 1/1... Discriminator Loss: 1.6051... Generator Loss: 0.4770
Epoch 1/1... Discriminator Loss: 1.9039... Generator Loss: 0.3181
Epoch 1/1... Discriminator Loss: 1.7078... Generator Loss: 0.5282
Epoch 1/1... Discriminator Loss: 1.7615... Generator Loss: 0.4888
Epoch 1/1... Discriminator Loss: 1.6394... Generator Loss: 0.5884
Epoch 1/1... Discriminator Loss: 1.5668... Generator Loss: 0.8478
Epoch 1/1... Discriminator Loss: 1.6420... Generator Loss: 0.6022
Epoch 1/1... Discriminator Loss: 1.5486... Generator Loss: 0.7089
Epoch 1/1... Discriminator Loss: 1.6031... Generator Loss: 0.4042
Epoch 1/1... Discriminator Loss: 1.4976... Generator Loss: 0.5983
Epoch 1/1... Discriminator Loss: 1.3723... Generator Loss: 0.7190
Epoch 1/1... Discriminator Loss: 1.4674... Generator Loss: 0.6354
Epoch 1/1... Discriminator Loss: 1.7974... Generator Loss: 0.3303
Epoch 1/1... Discriminator Loss: 1.3463... Generator Loss: 0.9712
Epoch 1/1... Discriminator Loss: 1.4881... Generator Loss: 0.6872
Epoch 1/1... Discriminator Loss: 1.5615... Generator Loss: 0.6486
Epoch 1/1... Discriminator Loss: 1.7083... Generator Loss: 0.3688
Epoch 1/1... Discriminator Loss: 1.9153... Generator Loss: 1.3852
Epoch 1/1... Discriminator Loss: 1.6992... Generator Loss: 0.4701
Epoch 1/1... Discriminator Loss: 1.6225... Generator Loss: 1.0997
Epoch 1/1... Discriminator Loss: 1.4918... Generator Loss: 0.6913
Epoch 1/1... Discriminator Loss: 1.6989... Generator Loss: 0.4247
Epoch 1/1... Discriminator Loss: 1.5128... Generator Loss: 0.5412
Epoch 1/1... Discriminator Loss: 1.5042... Generator Loss: 0.7873
Epoch 1/1... Discriminator Loss: 1.3865... Generator Loss: 0.7175
Epoch 1/1... Discriminator Loss: 2.1726... Generator Loss: 0.1915
Epoch 1/1... Discriminator Loss: 1.6600... Generator Loss: 0.5595
Epoch 1/1... Discriminator Loss: 1.2816... Generator Loss: 0.7992
Epoch 1/1... Discriminator Loss: 1.4734... Generator Loss: 0.9471
Epoch 1/1... Discriminator Loss: 1.5015... Generator Loss: 0.4775
Epoch 1/1... Discriminator Loss: 1.5781... Generator Loss: 1.2481
Epoch 1/1... Discriminator Loss: 1.3905... Generator Loss: 0.8718
Epoch 1/1... Discriminator Loss: 1.3894... Generator Loss: 0.6305
Epoch 1/1... Discriminator Loss: 1.2970... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 1.6529... Generator Loss: 2.2087
Epoch 1/1... Discriminator Loss: 1.2362... Generator Loss: 0.6750
Epoch 1/1... Discriminator Loss: 1.4367... Generator Loss: 0.5150
Epoch 1/1... Discriminator Loss: 0.9212... Generator Loss: 1.1278
Epoch 1/1... Discriminator Loss: 3.2228... Generator Loss: 2.5710
Epoch 1/1... Discriminator Loss: 1.4680... Generator Loss: 1.0883
Epoch 1/1... Discriminator Loss: 1.3668... Generator Loss: 1.2663
Epoch 1/1... Discriminator Loss: 1.5987... Generator Loss: 1.8029
Epoch 1/1... Discriminator Loss: 1.2389... Generator Loss: 1.1694
Epoch 1/1... Discriminator Loss: 1.2687... Generator Loss: 0.8626
Epoch 1/1... Discriminator Loss: 1.3119... Generator Loss: 0.5303
Epoch 1/1... Discriminator Loss: 1.5504... Generator Loss: 0.5704
Epoch 1/1... Discriminator Loss: 1.8031... Generator Loss: 0.4417
Epoch 1/1... Discriminator Loss: 1.3652... Generator Loss: 0.5963
Epoch 1/1... Discriminator Loss: 1.4450... Generator Loss: 0.6551
Epoch 1/1... Discriminator Loss: 1.8936... Generator Loss: 0.2497
Epoch 1/1... Discriminator Loss: 1.7257... Generator Loss: 0.4430
Epoch 1/1... Discriminator Loss: 1.2793... Generator Loss: 0.7150
Epoch 1/1... Discriminator Loss: 1.1592... Generator Loss: 0.8624
Epoch 1/1... Discriminator Loss: 1.0577... Generator Loss: 1.4503
Epoch 1/1... Discriminator Loss: 1.5866... Generator Loss: 0.3953
Epoch 1/1... Discriminator Loss: 1.6265... Generator Loss: 0.3551
Epoch 1/1... Discriminator Loss: 1.3638... Generator Loss: 0.8965
Epoch 1/1... Discriminator Loss: 1.0908... Generator Loss: 1.6319
Epoch 1/1... Discriminator Loss: 1.7462... Generator Loss: 0.3253
Epoch 1/1... Discriminator Loss: 1.4781... Generator Loss: 0.4788
Epoch 1/1... Discriminator Loss: 1.1599... Generator Loss: 0.7729
Epoch 1/1... Discriminator Loss: 1.2187... Generator Loss: 0.6534
Epoch 1/1... Discriminator Loss: 1.3267... Generator Loss: 0.8956
Epoch 1/1... Discriminator Loss: 1.4396... Generator Loss: 0.7268
Epoch 1/1... Discriminator Loss: 1.2865... Generator Loss: 0.8709
Epoch 1/1... Discriminator Loss: 1.1872... Generator Loss: 1.0513
Epoch 1/1... Discriminator Loss: 1.6364... Generator Loss: 0.3700
Epoch 1/1... Discriminator Loss: 1.3394... Generator Loss: 0.6761
Epoch 1/1... Discriminator Loss: 1.4969... Generator Loss: 0.7297
Epoch 1/1... Discriminator Loss: 1.3877... Generator Loss: 0.8283
Epoch 1/1... Discriminator Loss: 1.5409... Generator Loss: 0.8501
Epoch 1/1... Discriminator Loss: 1.4732... Generator Loss: 0.6894
Epoch 1/1... Discriminator Loss: 1.3670... Generator Loss: 0.9053
Epoch 1/1... Discriminator Loss: 1.4933... Generator Loss: 0.7023
Epoch 1/1... Discriminator Loss: 1.5689... Generator Loss: 0.5908
Epoch 1/1... Discriminator Loss: 1.4932... Generator Loss: 0.7803
Epoch 1/1... Discriminator Loss: 1.5628... Generator Loss: 0.5064
Epoch 1/1... Discriminator Loss: 1.4142... Generator Loss: 0.8448
Epoch 1/1... Discriminator Loss: 1.4707... Generator Loss: 0.6944
Epoch 1/1... Discriminator Loss: 1.3312... Generator Loss: 0.7217
Epoch 1/1... Discriminator Loss: 1.3612... Generator Loss: 0.6310
Epoch 1/1... Discriminator Loss: 1.4254... Generator Loss: 0.5794
Epoch 1/1... Discriminator Loss: 1.3697... Generator Loss: 0.8073
Epoch 1/1... Discriminator Loss: 1.4008... Generator Loss: 0.5143
Epoch 1/1... Discriminator Loss: 1.5632... Generator Loss: 0.4205
Epoch 1/1... Discriminator Loss: 1.3726... Generator Loss: 0.6423
Epoch 1/1... Discriminator Loss: 1.4936... Generator Loss: 1.1271
Epoch 1/1... Discriminator Loss: 2.2284... Generator Loss: 0.1878
Epoch 1/1... Discriminator Loss: 1.3345... Generator Loss: 1.1112
Epoch 1/1... Discriminator Loss: 1.3904... Generator Loss: 0.7021
Epoch 1/1... Discriminator Loss: 1.4044... Generator Loss: 0.5529
Epoch 1/1... Discriminator Loss: 1.3660... Generator Loss: 0.8915
Epoch 1/1... Discriminator Loss: 1.8503... Generator Loss: 0.5660
Epoch 1/1... Discriminator Loss: 1.3959... Generator Loss: 0.6618
Epoch 1/1... Discriminator Loss: 1.4532... Generator Loss: 0.9191
Epoch 1/1... Discriminator Loss: 1.4122... Generator Loss: 0.8217
Epoch 1/1... Discriminator Loss: 1.4280... Generator Loss: 0.8223
Epoch 1/1... Discriminator Loss: 1.4099... Generator Loss: 0.9227
Epoch 1/1... Discriminator Loss: 1.3817... Generator Loss: 0.7803
Epoch 1/1... Discriminator Loss: 1.4334... Generator Loss: 0.6212
Epoch 1/1... Discriminator Loss: 1.2589... Generator Loss: 0.9928
Epoch 1/1... Discriminator Loss: 1.3692... Generator Loss: 1.1199
Epoch 1/1... Discriminator Loss: 1.4469... Generator Loss: 0.5831
Epoch 1/1... Discriminator Loss: 1.7158... Generator Loss: 0.4135
Epoch 1/1... Discriminator Loss: 1.3010... Generator Loss: 1.1335
Epoch 1/1... Discriminator Loss: 2.0992... Generator Loss: 0.3075
Epoch 1/1... Discriminator Loss: 1.4166... Generator Loss: 0.7003
Epoch 1/1... Discriminator Loss: 1.5235... Generator Loss: 0.4957
Epoch 1/1... Discriminator Loss: 1.7441... Generator Loss: 0.3374
Epoch 1/1... Discriminator Loss: 1.3471... Generator Loss: 0.5413
Epoch 1/1... Discriminator Loss: 1.2140... Generator Loss: 0.8706
Epoch 1/1... Discriminator Loss: 1.4733... Generator Loss: 0.6937
Epoch 1/1... Discriminator Loss: 1.4149... Generator Loss: 0.4910
Epoch 1/1... Discriminator Loss: 1.5182... Generator Loss: 0.4493
Epoch 1/1... Discriminator Loss: 1.4063... Generator Loss: 0.9281
Epoch 1/1... Discriminator Loss: 1.7589... Generator Loss: 0.4982
Epoch 1/1... Discriminator Loss: 1.4242... Generator Loss: 0.5559
Epoch 1/1... Discriminator Loss: 1.3754... Generator Loss: 0.6696
Epoch 1/1... Discriminator Loss: 1.6184... Generator Loss: 1.6660
Epoch 1/1... Discriminator Loss: 1.4551... Generator Loss: 0.6680
Epoch 1/1... Discriminator Loss: 2.0989... Generator Loss: 0.2412
Epoch 1/1... Discriminator Loss: 2.4060... Generator Loss: 0.1697
Epoch 1/1... Discriminator Loss: 1.3469... Generator Loss: 0.6802
Epoch 1/1... Discriminator Loss: 1.5093... Generator Loss: 0.5274
Epoch 1/1... Discriminator Loss: 1.5669... Generator Loss: 0.5437
Epoch 1/1... Discriminator Loss: 1.5208... Generator Loss: 0.6009
Epoch 1/1... Discriminator Loss: 1.4755... Generator Loss: 0.7742
Epoch 1/1... Discriminator Loss: 1.4465... Generator Loss: 0.6583
Epoch 1/1... Discriminator Loss: 1.4810... Generator Loss: 0.8065
Epoch 1/1... Discriminator Loss: 1.4038... Generator Loss: 0.6271
Epoch 1/1... Discriminator Loss: 1.8494... Generator Loss: 0.3525
Epoch 1/1... Discriminator Loss: 1.7035... Generator Loss: 0.3958
Epoch 1/1... Discriminator Loss: 1.4730... Generator Loss: 0.7749
Epoch 1/1... Discriminator Loss: 2.7412... Generator Loss: 0.1047
Epoch 1/1... Discriminator Loss: 1.4473... Generator Loss: 0.6530
Epoch 1/1... Discriminator Loss: 1.3980... Generator Loss: 0.7105
Epoch 1/1... Discriminator Loss: 1.4811... Generator Loss: 0.9102
Epoch 1/1... Discriminator Loss: 1.4400... Generator Loss: 0.6392
Epoch 1/1... Discriminator Loss: 1.6206... Generator Loss: 0.7654
Epoch 1/1... Discriminator Loss: 1.3077... Generator Loss: 0.6737
Epoch 1/1... Discriminator Loss: 1.4279... Generator Loss: 0.8127
Epoch 1/1... Discriminator Loss: 1.7157... Generator Loss: 0.4373
Epoch 1/1... Discriminator Loss: 1.6219... Generator Loss: 0.4300
Epoch 1/1... Discriminator Loss: 1.4805... Generator Loss: 0.7900
Epoch 1/1... Discriminator Loss: 1.7386... Generator Loss: 1.2967
Epoch 1/1... Discriminator Loss: 1.6762... Generator Loss: 0.4015
Epoch 1/1... Discriminator Loss: 1.5797... Generator Loss: 0.5680
Epoch 1/1... Discriminator Loss: 1.6171... Generator Loss: 0.5169
Epoch 1/1... Discriminator Loss: 1.3505... Generator Loss: 0.8736
Epoch 1/1... Discriminator Loss: 1.3817... Generator Loss: 0.7083
Epoch 1/1... Discriminator Loss: 2.4988... Generator Loss: 1.3594
Epoch 1/1... Discriminator Loss: 1.3571... Generator Loss: 0.7697
Epoch 1/1... Discriminator Loss: 1.4956... Generator Loss: 0.7218
Epoch 1/1... Discriminator Loss: 1.4296... Generator Loss: 0.9153
Epoch 1/1... Discriminator Loss: 1.4932... Generator Loss: 0.6206
Epoch 1/1... Discriminator Loss: 1.4345... Generator Loss: 0.6302
Epoch 1/1... Discriminator Loss: 1.4421... Generator Loss: 0.7610
Epoch 1/1... Discriminator Loss: 1.4870... Generator Loss: 0.4871
Epoch 1/1... Discriminator Loss: 1.4322... Generator Loss: 0.6450
Epoch 1/1... Discriminator Loss: 1.4968... Generator Loss: 0.6087
Epoch 1/1... Discriminator Loss: 2.1554... Generator Loss: 0.2580
Epoch 1/1... Discriminator Loss: 1.5388... Generator Loss: 0.6246
Epoch 1/1... Discriminator Loss: 1.4099... Generator Loss: 0.6330
Epoch 1/1... Discriminator Loss: 1.4023... Generator Loss: 0.7319
Epoch 1/1... Discriminator Loss: 1.4350... Generator Loss: 0.6288
Epoch 1/1... Discriminator Loss: 1.9386... Generator Loss: 1.2825
Epoch 1/1... Discriminator Loss: 1.5261... Generator Loss: 0.5562
Epoch 1/1... Discriminator Loss: 1.4451... Generator Loss: 1.0449
Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 0.7270
Epoch 1/1... Discriminator Loss: 1.2860... Generator Loss: 0.8191
Epoch 1/1... Discriminator Loss: 1.6377... Generator Loss: 0.4379
Epoch 1/1... Discriminator Loss: 1.4801... Generator Loss: 0.6174
Epoch 1/1... Discriminator Loss: 1.4005... Generator Loss: 0.6829
Epoch 1/1... Discriminator Loss: 1.4390... Generator Loss: 0.5528
Epoch 1/1... Discriminator Loss: 1.4165... Generator Loss: 0.6277
Epoch 1/1... Discriminator Loss: 1.8551... Generator Loss: 1.6227
Epoch 1/1... Discriminator Loss: 1.3119... Generator Loss: 1.1750
Epoch 1/1... Discriminator Loss: 1.2733... Generator Loss: 0.6340
Epoch 1/1... Discriminator Loss: 1.3196... Generator Loss: 0.8948
Epoch 1/1... Discriminator Loss: 1.5819... Generator Loss: 0.5892
Epoch 1/1... Discriminator Loss: 1.5018... Generator Loss: 1.0162
Epoch 1/1... Discriminator Loss: 1.3816... Generator Loss: 0.7053
Epoch 1/1... Discriminator Loss: 1.7171... Generator Loss: 1.3099
Epoch 1/1... Discriminator Loss: 1.3703... Generator Loss: 1.0635
Epoch 1/1... Discriminator Loss: 1.5837... Generator Loss: 0.3846
Epoch 1/1... Discriminator Loss: 1.4201... Generator Loss: 0.6687
Epoch 1/1... Discriminator Loss: 1.3312... Generator Loss: 0.7447
Epoch 1/1... Discriminator Loss: 1.5207... Generator Loss: 0.6297
Epoch 1/1... Discriminator Loss: 1.4227... Generator Loss: 0.9170
Epoch 1/1... Discriminator Loss: 1.4376... Generator Loss: 0.7690
Epoch 1/1... Discriminator Loss: 1.2525... Generator Loss: 0.8782
Epoch 1/1... Discriminator Loss: 1.5092... Generator Loss: 0.5916
Epoch 1/1... Discriminator Loss: 1.5622... Generator Loss: 0.5492
Epoch 1/1... Discriminator Loss: 3.2005... Generator Loss: 0.0895
Epoch 1/1... Discriminator Loss: 1.4547... Generator Loss: 0.6829
Epoch 1/1... Discriminator Loss: 1.9426... Generator Loss: 0.3181
Epoch 1/1... Discriminator Loss: 1.4443... Generator Loss: 0.6532
Epoch 1/1... Discriminator Loss: 1.3175... Generator Loss: 0.8651
Epoch 1/1... Discriminator Loss: 1.3909... Generator Loss: 0.5769
Epoch 1/1... Discriminator Loss: 1.3743... Generator Loss: 0.5710
Epoch 1/1... Discriminator Loss: 1.4545... Generator Loss: 0.6045
Epoch 1/1... Discriminator Loss: 1.5257... Generator Loss: 1.0351
Epoch 1/1... Discriminator Loss: 1.5272... Generator Loss: 0.7440
Epoch 1/1... Discriminator Loss: 1.4769... Generator Loss: 0.5787
Epoch 1/1... Discriminator Loss: 1.7168... Generator Loss: 0.3926
Epoch 1/1... Discriminator Loss: 1.5528... Generator Loss: 0.5988
Epoch 1/1... Discriminator Loss: 1.4865... Generator Loss: 0.5875
Epoch 1/1... Discriminator Loss: 1.5110... Generator Loss: 0.5684
Epoch 1/1... Discriminator Loss: 1.5457... Generator Loss: 1.1209
Epoch 1/1... Discriminator Loss: 1.5312... Generator Loss: 0.5724
Epoch 1/1... Discriminator Loss: 1.3659... Generator Loss: 0.6086
Epoch 1/1... Discriminator Loss: 1.3848... Generator Loss: 0.8770
Epoch 1/1... Discriminator Loss: 1.5118... Generator Loss: 0.6566
Epoch 1/1... Discriminator Loss: 1.5923... Generator Loss: 0.5071
Epoch 1/1... Discriminator Loss: 1.5555... Generator Loss: 0.5519
Epoch 1/1... Discriminator Loss: 1.4330... Generator Loss: 0.8973
Epoch 1/1... Discriminator Loss: 1.5595... Generator Loss: 0.5099
Epoch 1/1... Discriminator Loss: 1.4175... Generator Loss: 0.5241
Epoch 1/1... Discriminator Loss: 1.4525... Generator Loss: 0.9594
Epoch 1/1... Discriminator Loss: 1.6218... Generator Loss: 0.4284
Epoch 1/1... Discriminator Loss: 1.7494... Generator Loss: 0.3463
Epoch 1/1... Discriminator Loss: 1.5029... Generator Loss: 0.5131
Epoch 1/1... Discriminator Loss: 1.4086... Generator Loss: 0.6500
Epoch 1/1... Discriminator Loss: 2.6964... Generator Loss: 0.1449
Epoch 1/1... Discriminator Loss: 1.3881... Generator Loss: 0.7935
Epoch 1/1... Discriminator Loss: 1.5661... Generator Loss: 0.4885
Epoch 1/1... Discriminator Loss: 1.6343... Generator Loss: 0.4374
Epoch 1/1... Discriminator Loss: 1.4262... Generator Loss: 0.5434
Epoch 1/1... Discriminator Loss: 1.6834... Generator Loss: 0.4010
Epoch 1/1... Discriminator Loss: 1.7019... Generator Loss: 0.4311
Epoch 1/1... Discriminator Loss: 1.4831... Generator Loss: 1.0671
Epoch 1/1... Discriminator Loss: 1.3174... Generator Loss: 0.7465
Epoch 1/1... Discriminator Loss: 1.4575... Generator Loss: 0.5820
Epoch 1/1... Discriminator Loss: 1.4418... Generator Loss: 0.6509
Epoch 1/1... Discriminator Loss: 1.2513... Generator Loss: 0.7494
Epoch 1/1... Discriminator Loss: 1.5923... Generator Loss: 0.5109
Epoch 1/1... Discriminator Loss: 1.2335... Generator Loss: 0.6204
Epoch 1/1... Discriminator Loss: 1.4914... Generator Loss: 0.5598
Epoch 1/1... Discriminator Loss: 1.2972... Generator Loss: 0.6410
Epoch 1/1... Discriminator Loss: 1.3025... Generator Loss: 0.7194
Epoch 1/1... Discriminator Loss: 1.9808... Generator Loss: 0.3338
Epoch 1/1... Discriminator Loss: 1.6043... Generator Loss: 0.7388
Epoch 1/1... Discriminator Loss: 1.4085... Generator Loss: 0.5954
Epoch 1/1... Discriminator Loss: 1.3512... Generator Loss: 0.6533
Epoch 1/1... Discriminator Loss: 1.5179... Generator Loss: 0.4116
Epoch 1/1... Discriminator Loss: 1.3528... Generator Loss: 0.7786
Epoch 1/1... Discriminator Loss: 1.1674... Generator Loss: 0.7866
Epoch 1/1... Discriminator Loss: 1.6350... Generator Loss: 0.4850
Epoch 1/1... Discriminator Loss: 1.4815... Generator Loss: 0.9040
Epoch 1/1... Discriminator Loss: 1.7117... Generator Loss: 0.4238
Epoch 1/1... Discriminator Loss: 1.4347... Generator Loss: 0.5681
Epoch 1/1... Discriminator Loss: 1.6268... Generator Loss: 0.4658
Epoch 1/1... Discriminator Loss: 2.0206... Generator Loss: 1.5484
Epoch 1/1... Discriminator Loss: 1.4942... Generator Loss: 0.6437
Epoch 1/1... Discriminator Loss: 1.4262... Generator Loss: 0.7604
Epoch 1/1... Discriminator Loss: 1.5026... Generator Loss: 0.5577
Epoch 1/1... Discriminator Loss: 1.5049... Generator Loss: 0.5640
Epoch 1/1... Discriminator Loss: 1.5058... Generator Loss: 0.5945
Epoch 1/1... Discriminator Loss: 1.8421... Generator Loss: 0.3697
Epoch 1/1... Discriminator Loss: 1.5511... Generator Loss: 0.5855
Epoch 1/1... Discriminator Loss: 1.5527... Generator Loss: 0.6619
Epoch 1/1... Discriminator Loss: 1.3288... Generator Loss: 0.9286
Epoch 1/1... Discriminator Loss: 1.4605... Generator Loss: 0.5835
Epoch 1/1... Discriminator Loss: 1.4985... Generator Loss: 0.7698
Epoch 1/1... Discriminator Loss: 1.3319... Generator Loss: 0.5439
Epoch 1/1... Discriminator Loss: 1.4930... Generator Loss: 0.4236
Epoch 1/1... Discriminator Loss: 1.5005... Generator Loss: 0.6789
Epoch 1/1... Discriminator Loss: 1.3957... Generator Loss: 0.6411
Epoch 1/1... Discriminator Loss: 3.5287... Generator Loss: 0.0524
Epoch 1/1... Discriminator Loss: 1.4237... Generator Loss: 0.8079
Epoch 1/1... Discriminator Loss: 1.4409... Generator Loss: 0.6643
Epoch 1/1... Discriminator Loss: 1.4813... Generator Loss: 0.6489
Epoch 1/1... Discriminator Loss: 1.4987... Generator Loss: 0.6884
Epoch 1/1... Discriminator Loss: 1.6188... Generator Loss: 0.5242
Epoch 1/1... Discriminator Loss: 1.3324... Generator Loss: 0.9353
Epoch 1/1... Discriminator Loss: 2.7885... Generator Loss: 0.1626
Epoch 1/1... Discriminator Loss: 1.4725... Generator Loss: 0.6577
Epoch 1/1... Discriminator Loss: 1.6707... Generator Loss: 0.3767
Epoch 1/1... Discriminator Loss: 1.8038... Generator Loss: 0.2854
Epoch 1/1... Discriminator Loss: 1.5454... Generator Loss: 0.5874
Epoch 1/1... Discriminator Loss: 1.7853... Generator Loss: 0.4259
Epoch 1/1... Discriminator Loss: 2.1745... Generator Loss: 0.2606
Epoch 1/1... Discriminator Loss: 1.5032... Generator Loss: 0.6078
Epoch 1/1... Discriminator Loss: 1.4180... Generator Loss: 0.7199
Epoch 1/1... Discriminator Loss: 1.3631... Generator Loss: 0.8515
Epoch 1/1... Discriminator Loss: 1.7055... Generator Loss: 0.4569
Epoch 1/1... Discriminator Loss: 1.7324... Generator Loss: 0.3346
Epoch 1/1... Discriminator Loss: 1.4681... Generator Loss: 0.6578
Epoch 1/1... Discriminator Loss: 1.5562... Generator Loss: 0.4849
Epoch 1/1... Discriminator Loss: 1.4938... Generator Loss: 0.8454
Epoch 1/1... Discriminator Loss: 1.1931... Generator Loss: 0.8342
Epoch 1/1... Discriminator Loss: 1.5861... Generator Loss: 1.0702
Epoch 1/1... Discriminator Loss: 1.4961... Generator Loss: 0.6500
Epoch 1/1... Discriminator Loss: 1.5562... Generator Loss: 0.5162
Epoch 1/1... Discriminator Loss: 1.5138... Generator Loss: 0.4516
Epoch 1/1... Discriminator Loss: 1.7442... Generator Loss: 0.4861

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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